Methods for generating natural language processing systems

Abstract

Methods are presented for generating a natural language model. The method may comprise: ingesting training data representative of documents to be analyzed by the natural language model, generating a hierarchical data structure comprising at least two topical nodes within which the training data is to be subdivided into by the natural language model, selecting a plurality of documents among the training data to be annotated, generating an annotation prompt for each document configured to elicit an annotation about said document indicating which node among the at least two topical nodes said document is to be classified into, receiving the annotation based on the annotation prompt; and generating the natural language model using an adaptive machine learning process configured to determine patterns among the annotations for how the documents in the training data are to be subdivided according to the at least two topical nodes of the hierarchical data structure.

Description

CROSS REFERENCES TO RELATED APPLICATIONS[0001]This application claims the benefits of U.S. Provisional Application 62 / 089,736, filed Dec. 9, 2014, and titled, “METHODS AND SYSTEMS FOR ANNOTATING NATURAL LANGUAGE PROCESSING,” U.S. Provisional Application 62 / 089,742, filed Dec. 9, 2014, and titled, “METHODS AND SYSTEMS FOR IMPROVING MACHINE PERFORMANCE IN NATURAL LANGUAGE PROCESSING,” U.S. Provisional Application 62 / 089,745, filed Dec. 9, 2014, and titled, “METHODS AND SYSTEMS FOR IMPROVING FUNCTIONALITY IN NATURAL LANGUAGE PROCESSING,” and U.S. Provisional Application 62 / 089,747, filed Dec. 9, 2014, and titled, “METHODS AND SYSTEMS FOR SUPPORTING NATURAL LANGUAGE PROCESSING,” the disclosures of which are incorporated herein by reference in their entireties and for all purposes.[0002]This application is also related to US non provisional applications (Attorney Docket No. 1402805.00007_IDB007), titled “ARCHITECTURES FOR NATURAL LANGUAGE PROCESSING,” (Attorney Docket No. 1402805.00012_I...

AI Technical Summary

Benefits of technology

[0018]In some embodiments, another method for updating a natural language model is presented. This method may include: utilizing the natural language model to identify topical content of untested data and to classify said untested data into at least two topical nodes of a hierarchical data structure according to the identified topical content of the untrained data, the hierarchical data structure comprising the at least two topical nodes, wherein the at least two topical nodes represent partitions organized by two or more topical themes among the topical content of the untested data within which the untested data is to be subdivided into; determining that the natural language model classifies at least a subset of the untested data into the at least two topical nodes with a low degree of certainty; and modifying the natural language model with updated data, the updated data comprising a subset of the untested data that the natural language model has classified with a low degree of certainty.

[0019]In some embodiments, a non-transitory computer readable medium is presented comprising instructions that, when executed by a processor, cause the processor to perform operations comprising: ingesting training data representative of documents to be analyzed by a natural...

Problems solved by technology

However, programming computers to process human-readable language has proven to be far more difficult than imagined, particularly as languages continue to change and evolve, and the meaning of words and phrases are more ambiguous and nuanced than assumed.

A ...

Images